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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A) Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
B) Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
C) Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
D) Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.

2. You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A) DLProf requires significant modifications to the source code to collect profiling data.
B) DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
C) DLProf is primarily designed for debugging model accuracy rather than performance analysis.
D) DLProf only works with TensorFlow models and does not support PyTorch-based workloads.

3. A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A) Limit the benchmark to small datasets since GPUs excel at parallel processing.
B) Focus only on processing speed without considering resource consumption differences between frameworks.
C) Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
D) Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.

4. You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A) Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
B) Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
C) Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
D) Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.

5. You need to process a dataset containing 10 billion rows, applying complex transformations and aggregations. The dataset does not fit into RAM, and you need an efficient, scalable solution for parallel processing.
Which of the following is the best choice?

A) Dask
B) Pandas
C) PySpark
D) Vaex

Solutions: